Abstract Details
Activity Number:
|
150
|
Type:
|
Invited
|
Date/Time:
|
Monday, August 10, 2015 : 10:30 AM to 12:20 PM
|
Sponsor:
|
Section on Statistics and the Environment
|
Abstract #314679
|
|
Title:
|
Computational Challenges with Big Environmental Data
|
Author(s):
|
Marc Genton*
|
Companies:
|
KAUST
|
Keywords:
|
big data ;
climate model output ;
computational statistics ;
spatial extremes
|
Abstract:
|
In this talk, we discuss two types of computational challenges arising from big environmental data. The first type occurs with multivariate or spatial extremes. Indeed, inference for max-stable processes observed at a large collection of locations is among the most challenging problems in computational statistics, and current approaches typically rely on less expensive composite likelihoods constructed from small subsets of data. We explore the limits of modern state-of-the-art computational facilities to perform full likelihood inference and to efficiently evaluate high-order composite likelihoods. The second type of challenges occurs with the emulation of climate model outputs. We consider fitting a statistical model to 1 billion global 3D spatio-temporal temperature data using a distributed computing approach.
|
Authors who are presenting talks have a * after their name.
Back to the full JSM 2015 program
|
For program information, contact the JSM Registration Department or phone (888) 231-3473.
For Professional Development information, contact the Education Department.
The views expressed here are those of the individual authors and not necessarily those of the JSM sponsors, their officers, or their staff.
2015 JSM Online Program Home
ASA Meetings Department
732 North Washington Street, Alexandria, VA 22314
(703) 684-1221 • meetings@amstat.org
Copyright © American Statistical Association.